Selection of Data Sets by Quality and its Role in Climate Research
The selection of good quality data sets is essential for all kinds of data processing involved in climate research. The presence of low quality data can lead to incorrect results and conclusions.
Data quality has two main features: accuracy and precision. It is possible to estimate data accuracy based on the precision detected. The Real Precision method (Petrovic, 1998) is used and modified here in order to show information on data accuracy based on the determined real precision. The low quality data sets have an increased probability of inaccurate data. Such data sets should not be treated in the same way as data estimated as accurate during further data processing.
The example given in this paper shows the influence of low quality data sets on the series. Careful examination of data might lead to conclusions on factors that influence their quality. Such factors cannot always be featured in meta data (i.e. rounding effect). Therefore, every break point of data quality should be considered as a break point in the homogenisation of data series.
The elimination of inaccurate data might result in shorter series or more frequent missing data. Such data series are much harder or almost impossible to homogenise. Still, the remaining data could be used in the examination of single situations that may provide a good base for more accurate climate studies. It is necessary to select data by their quality prior to homogenisation and further climate research.
KeywordsBreak Point Climate Research Data Precision Inaccurate Data Tick Mark
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